Senior Principal Scientist, Oligo Design and Data Science

Role Summary

Senior Principal Scientist, Oligo Design and Data Science analyzing large-scale screening data and applying machine learning to inform drug discovery. Responsible for architecture and implementation of DNA Encoded platforms informatics and driving data strategy for hit-finding at GSK.

Responsibilities

• Drive data science initiatives to support informed decision-making in active early-stage small molecule and oligonucleotide discovery projects.
• Collaborate with laboratory scientists to build data infrastructure, develop decision-making heuristics, and implement tracking systems for early discovery oligonucleotide and DEL projects, supporting workflows from initial screening through candidate selection.
• Collaborate closely with research tech and AI/ML teams to architect, develop, and optimize predictive informatics platforms that enable scalable data integration, advanced statistical analytics, and actionable insights for therapeutic discovery.

Qualifications

• PhD in computational science, bioinformatics, cheminformatics, computer science, or a closely related discipline.
• Experience in cheminformatics and DNA-encoded library (DEL) data analysis, including the application of advanced statistical and computational methods to large-scale biological datasets.
• Experience developing scientific applications using Python (such as pandas, scikit-learn, Django), SQL, and deploying solutions on modern cloud infrastructure.
• On-site presence of 2‚Äì3 days per week, as required for team collaboration and project delivery.

Preferred Qualifications

• Experience leading platform development initiatives that integrate research technology, artificial intelligence, and machine learning for scalable data analysis and informatics solutions.
• Significant contributions to open-source scientific software projects or recognized achievement in computational life science competitions (e.g., Kaggle, TopCoder, DREAM Challenge).
• Expertise in the design and optimization of automated ETL pipelines for processing terabyte-scale sequencing or screening data.
• Advanced knowledge of predictive modeling, Bayesian statistics, and deep learning approaches for hit identification and structure-activity relationship prediction.
• Demonstrated success in cross-functional communication, matrixed collaboration, and thought leadership within multidisciplinary teams.
• Strong analytical and problem-solving skills, with a track record of translating complex biological questions into actionable computational solutions.
• Ability to work collaboratively in cross-functional teams, communicating effectively with experts in chemistry, biology, biophysics, and data science.
• Experience with analysis of siRNA knockdown screens or CRISPR knockout libraries.

Education

• PhD in computational science, bioinformatics, cheminformatics, computer science, or closely related field.

Additional Requirements

• On-site presence of 2‚Äì3 days per week, as required for team collaboration and project delivery.